Translational research in the field of inherited arrhythmias
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | Inherited arrhythmias represent relatively rare, but life-threatening cardiac pathologies that are often associated with variants in cardiac ionic channel genes. A translational approach is essential to reveal the underlying arrhythmogenic mechanism and to find an improved treatment in the future. Since 2016, we have performed functional analysis in selected arrhythmia-associated heterozygous genetic variants, including the patch clamp and microelectrode array techniques and confocal microscopy, either on human ionic channels transfected in a cell line or on hiPSC-derived cardiomyocytes. Detailed analysis was performed in two KCNQ1 variants associated with LQTS. T309I resulted in a complete loss of function in the homozygous setting (impaired channel trafficking) and a dominant-negative effect in the heterozygous setting. In contrast, R562S showed preserved channel trafficking and, in the heterozygous setting, haploinsufficiency. The physiologically important beta-adrenergic stimulation was missing in R562S channels. In silico simulations suggested delayed afterdepolarizations as a likely arrhythmogenic mechanism in both variants. Cardiac ionic channel gene variants can be also detected in some patients suffering from the “true” idiopathic VF. We have recently started functional analysis in two probands, the first one carrying two KCNH2 variants (A228V and S1021Qfs*98) and the second one a single RYR2 variant (Y4734C). In Y4734C-RYR2 variant, the pilot data detected an irregular electric activity of the patient-specific cardiomyocytes at specific conditions; a detailed analysis will follow. Functional analysis is needed to reveal relationship between the identified genotype and phenotype. Identification of provoking circumstances that can result in unmasking of the phenotype in the “true” idiopathic VF could provide clinically-important data. |
| Related projects: |
|